870 citations · 1k across the 29 of their papers we have counts for
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-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction
Kerstin Hammernik, Jo Schlemper, Chen Qin +3
Purpose: To systematically investigate the influence of various data consistency layers, (semi-)supervised learning and ensembling strategies, defined in a -net, for accelerated…
-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction
Jo Schlemper, Chen Qin, Jinming Duan +2
We explore an ensembled -net for fast parallel MR imaging, including parallel coil networks, which perform implicit coil weighting, and sensitivity networks, involving explicit…
Deep learning for cardiac image segmentation: A review
Chen Chen, Chen Qin, Huaqi Qiu +4
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation pap…
Data consistency networks for (calibration-less) accelerated parallel MR image reconstruction
Jo Schlemper, Jinming Duan, Cheng Ouyang +4
We present simple reconstruction networks for multi-coil data by extending deep cascade of CNN's and exploiting the data consistency layer. In particular, we propose two variants,…
Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image
Chen Qin, Wenjia Bai, Jo Schlemper +4
Accelerating the acquisition of magnetic resonance imaging (MRI) is a challenging problem, and many works have been proposed to reconstruct images from undersampled k-space data. H…
k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-temporal Correlations
Chen Qin, Jo Schlemper, Jinming Duan +4
Dynamic magnetic resonance imaging (MRI) exhibits high correlations in k-space and time. In order to accelerate the dynamic MR imaging and to exploit k-t correlations from highly u…